This research activity will
utilize the least-squares identification technique for tuning the
dynamic models used for the controller design to achieve precision
pointing. System identification from measured frequency responses
involves fitting the parameterized transfer function matrix of a theoretical
linear time-invariant system to a measured transfer function matrix
at selected frequencies. A criterion is needed to qualify the error
between the theoretical and the measured responses at the test frequencies.
The least-squares identification technique does not require any a
priori error information. It correlates the measured data at the test
frequencies to minimize an average of modeling errors. Use of the
least-squares method makes it easy to compute the cost function of
the identification error, and to optimize with relative ease. These
issues are critical when dealing with large complex systems.
An actively controlled structure
such as a segmented reflector telescope involves a large number of
sensors and actuators, increasing significantly the likelihood of
a sensor/actuator failure. To address the substantial performance
degradation resulting from such a failure, a task will be included
to study suitable methodologies for failure detection, fault location
and isolation, and reconfiguration of the control system for performance
All control algorithms developed
in this research will be experimentally validated on the SPACE testbed.
As with the other research tasks, the progress and results of the
technical activities will be formally documented in annual reports
and a final comprehensive report will be issued at the completion
of the program in the fifth year.